The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control Article Swipe
Jasin Machkour
,
Michael Muma
,
Daniel P. Palomar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.sigpro.2025.109894
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.sigpro.2025.109894
We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the original predictors and multiple sets of randomly generated dummy predictors. A finite sample proof based on martingale theory for the FDR control property is provided. Numerical simulations confirm that the FDR is controlled at the target level while allowing for a high power. We prove under mild conditions that the dummies can be sampled from any univariate probability distribution with finite expectation and variance. The computational complexity of the proposed method is linear in the number of variables. The T-Rex selector outperforms state-of-the-art methods for FDR control on a simulated genome-wide association study (GWAS), while its sequential computation time is more than two orders of magnitude lower than that of the strongest benchmark methods. The open source R package TRexSelector containing the implementation of the T-Rex selector is available on CRAN.
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.sigpro.2025.109894
- OA Status
- hybrid
- Cited By
- 5
- References
- 62
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https://openalex.org/W4406320626Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.sigpro.2025.109894Digital Object Identifier
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The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate controlWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-14Full publication date if available
- Authors
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Jasin Machkour, Michael Muma, Daniel P. PalomarList of authors in order
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https://doi.org/10.1016/j.sigpro.2025.109894Publisher landing page
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.sigpro.2025.109894Direct OA link when available
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False discovery rate, Normalization property, Selection (genetic algorithm), Computer science, Feature selection, Algorithm, Artificial intelligence, Biology, Programming language, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 5Per-year citation counts (last 5 years)
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| abstract_inverted_index.proposed | 124 |
| abstract_inverted_index.randomly | 61 |
| abstract_inverted_index.selected | 31 |
| abstract_inverted_index.selector | 17, 135, 181 |
| abstract_inverted_index.variable | 9 |
| abstract_inverted_index.Numerical | 80 |
| abstract_inverted_index.available | 183 |
| abstract_inverted_index.benchmark | 167 |
| abstract_inverted_index.conducted | 49 |
| abstract_inverted_index.discovery | 23 |
| abstract_inverted_index.generated | 62 |
| abstract_inverted_index.magnitude | 160 |
| abstract_inverted_index.provided. | 79 |
| abstract_inverted_index.selection | 10 |
| abstract_inverted_index.selector, | 6 |
| abstract_inverted_index.simulated | 144 |
| abstract_inverted_index.solutions | 39 |
| abstract_inverted_index.strongest | 166 |
| abstract_inverted_index.variance. | 118 |
| abstract_inverted_index.complexity | 121 |
| abstract_inverted_index.conditions | 102 |
| abstract_inverted_index.containing | 175 |
| abstract_inverted_index.controlled | 87 |
| abstract_inverted_index.martingale | 71 |
| abstract_inverted_index.maximizing | 27 |
| abstract_inverted_index.predictors | 56 |
| abstract_inverted_index.sequential | 151 |
| abstract_inverted_index.terminated | 43 |
| abstract_inverted_index.univariate | 111 |
| abstract_inverted_index.variables. | 32, 132 |
| abstract_inverted_index.Experiments | 4 |
| abstract_inverted_index.association | 146 |
| abstract_inverted_index.combination | 52 |
| abstract_inverted_index.computation | 152 |
| abstract_inverted_index.expectation | 116 |
| abstract_inverted_index.experiments | 47 |
| abstract_inverted_index.genome-wide | 145 |
| abstract_inverted_index.outperforms | 136 |
| abstract_inverted_index.predictors. | 64 |
| abstract_inverted_index.probability | 112 |
| abstract_inverted_index.simulations | 81 |
| abstract_inverted_index.TRexSelector | 174 |
| abstract_inverted_index.distribution | 113 |
| abstract_inverted_index.experiments. | 45 |
| abstract_inverted_index.user-defined | 20 |
| abstract_inverted_index.computational | 120 |
| abstract_inverted_index.implementation | 177 |
| abstract_inverted_index.high-dimensional | 13 |
| abstract_inverted_index.state-of-the-art | 137 |
| abstract_inverted_index.Terminating-Random | 3 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.99228249 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |